1. Research question

Labor shortage is currently significant challenge in global agriculture, while both land use and labor is affecting agricultural food supply. In this report, we aim to analyze the trend of agricultural land use and labor from 1961 to 2019 in 16 key agricultural countries, investigating the relationship between labor issues in agriculture and land use to understand their interdependencies and effects on global agriculture.

2. Data set introduction

The dataset comprises agricultural land use, labor and food supply from 1961 to 2019 for 16 major agricultural production countries, providing insights into global agricultural trends and practices.The data sets with 944 observations and 6 variables were extracted from Our World in Data [https://ourworldindata.org/grapher/agricultural-labor-land]. It is an open-source database and can be used for research and analysis purposes.

The extracted dataset has variables from countries including Australia, Brazil, Canada, China, France, Germany, India, Mexico, Netherlands, New Zealand, Russia, South Africa, South Korea, Turkey, UK, USA from 1961 to 2019. There are three numerical variables– agricultural land use for the sum of croplands and permanent pastures for livestock grazing, agricultural labor for the number of people in agriculture, which includes hiredlabor and unpaid family labor, and agricultural food supply for the total output of agricultural products.

3. Data set description

The dataset contains 6 variables and 944 observations. The figure of the code is showing as the following.

Variable Names of dataset
Variable
Entity
Code
Year
ag_land_index
labor_index
food_supply_per_capita 
## 'data.frame':    2 obs. of  6 variables:
##  $ Entity                : chr  "Australia" "Australia"
##  $ Code                  : chr  "AUS" "AUS"
##  $ Year                  : int  1961 1962
##  $ ag_land_index         : num  91.9 93.9
##  $ labor_index           : num  140 140
##  $ food_supply_per_capita: num  1.36 1.67

4. Data Summary

Entity mean_land_use variance_land_use mean_labor variance_labor
Australia 100.10170 13.886922 125.54146 170.23519
Brazil 80.48894 283.270685 132.93399 325.02212
Canada 102.12398 13.154989 152.13008 1870.95271
China 90.94096 46.200156 134.58977 607.19428
France 99.47961 6.851704 231.63034 18205.34443
Germany 102.02014 5.943474 295.73544 31186.82249
India 87.12817 95.128728 91.31982 268.88501
Mexico 90.89976 110.093270 87.12339 133.00203
Netherlands 89.83478 48.221104 152.92678 1853.03063
New Zealand 147.89999 1345.215344 102.38662 91.59432

From this data summary, it can be observed that Germany and France have the highest mean labor values, suggesting a significant workforce in agriculture, with Germany also exhibiting the highest variance in labor, indicating large fluctuations or diversity in agricultural labor over the observed period. Additionally, New Zealand stands out with a notably high mean land use and the greatest variance in land use, which could imply extensive and varied agricultural practices in terms of land utilization.

5. Visualisations

Figure 1: Total Land Use vs. Total Labor of Main Agricultural Countries from 1961 to 2019

6. Conclusions

The analysis of agricultural data from 1961 to 2019 for major agricultural countries reveals significant trends in land use and labor. The data shows a steady increase in land use until 1990, followed by a period of fluctuation and a slight decrease, indicating changes in agricultural practices or policy shifts over the decades. In stark contrast, there is a notable and consistent decline in labor over the same period. This divergence suggests a move towards more efficient or mechanized farming techniques that require fewer workers, a response possibly driven by technological advancements, economic factors, and policy changes in the agricultural sector.

Moreover, the substantial decrease in labor, despite the initially increasing and later fluctuating land use, points to a potential restructuring in the agricultural workforce. This could be due to urban migration, changing demographic patterns, or shifts in the economic attractiveness of agricultural work.